With the rapid transition of Industry 4.0 to 5.0, modern industrial physio-chemical processes are characterized by two critical challenges: process safety and the quality of the final product. Traditional industrial monitoring methods have low reliability in accuracy and robustness, and they are inefficiently providing satisfactory results. This paper introduces a novel integration technique that employs machine learning (ML) to tackle the challenges associated with real industrial monitoring in physical and industrial processes. The proposed framework integrates distributed canonical correlation analysis - R-vine copula (DCCA-RVC), global local preserving projection (GLPP), and 2-Dimensional Deng information entropy (2-DDE). The framework's ability and productivity are assessed utilizing existing approaches such as wavelet-PCA, MRSAE, and DALSTM-AE and the new proposed novel integrated machine learning-based (DCCA-RVC) approach as benchmarks for model performance. The proposed novel approach has been validated by testing it on the ethanol-water system distillation column (DC) and Tennessee Eastman Process (TEP), utilizing it as actual industrial benchmarks. The results demonstrate that the novel integration ML-technique (DCCA-RVC) T22 – GLP monitoring graphs for the fault class type 1 in the distillation column showed a (FAR) of 0 %, a (FDR) of 100 %, a precision of 100 %, F1-score of 100 % and an accuracy of 100 %. However, for the TEP process failure event 13, the (FAR) was 0 %, the (FDR) was 99 %, the accuracy was 100 %, the F1-score was 99.5 %, and the accuracy was 99.5 %.

中文翻译：

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使用新颖的集成机器学习方法推进物理化学过程的工业监测


随着工业 4.0 向工业 5.0 的快速过渡，现代工业物理化学过程面临两个关键挑战：过程安全和最终产品的质量。传统的工业监测方法在准确性和稳健性方面可靠性较低，并且无法有效地提供令人满意的结果。本文介绍了一种新颖的集成技术，该技术采用机器学习 （ML） 来应对与物理和工业过程中的实际工业监控相关的挑战。所提出的框架集成了分布式典型相关分析 - R-vine copula （DCCA-RVC），全局局部保留投影（GLPP）和二维邓信息熵（2-DDE）。利用现有方法（如小波 PCA、MRSAE 和 DALSTM-AE）以及新提出的新型基于集成机器学习 （DCCA-RVC） 的方法作为模型性能的基准，评估了该框架的能力和生产力。通过在乙醇-水系统蒸馏塔 （DC） 和田纳西伊士曼工艺 （TEP） 上进行测试，验证了所提出的新方法，并将其用作实际的工业基准。结果表明，蒸馏塔中故障类别类型 1 的新型集成 ML 技术 （DCCA-RVC） T22 – GLP 监测图显示 （FAR） 为 0 %，a （FDR） 为 100 %，精度为 100 %，F1 评分为 100 %，准确度为 100 %。然而，对于 TEP 过程故障事件 13，（FAR） 为 0 %，（FDR） 为 99 %，准确率为 100 %，F1 评分为 99.5 %，准确率为 99.5 %。